ArticleCancer medicine2025
Multimodal Deep Learning Integrating Tumor Radiomics and Mediastinal Adiposity Improves Survival Prediction in Non-Small Cell Lung Cancer: A Prognostic Modeling Study.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hybrid deep learning time-to-event modeling of major adverse cardiovascular events using coronary artery calcium score scans.European journal of radiology artificial intelligence · 2026Article
- Handling missing data: AI approach for survival prediction in lung cancer despite missing data.NPJ digital medicine · 2026Article
- Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.NPJ digital medicine · 2026Article
- Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.Multidisciplinary respiratory medicine · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- A deep learning-based prognostic model for diffuse large B-cell lymphoma incorporating PET/CT imaging features.Frontiers in oncology · 2026Article
- Multimodal Deep Learning Integrating Tumor Radiomics and Mediastinal Adiposity Improves Survival Prediction in Non-Small Cell Lung Cancer: A Prognostic Modeling Study.Cancer medicine · 2025Article
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Abstract
background and purposePrognostic stratification in non-small cell lung cancer (NSCLC) presents considerable challenges due to tumor heterogeneity. Emerging evidence has proposed that adipose tissue may play a prognostic role in oncological outcomes. This study investigates the integration of deep learning (DL)-derived computed tomography (CT) imaging biomarkers with mediastinal adiposity metrics to develop a multimodal prognostic model for postoperative survival prediction in NSCLC patients.
methodsA retrospective cohort of 702 surgically resected NSCLC patients was analyzed. Tumor radiomic features were extracted using a DenseNet121 convolutional neural network architecture, while mediastinal fat area (MFA) was quantified through semiautomated segmentation using ImageJ software. A multimodal survival prediction model was developed through feature-level fusion of DL-extracted tumor characteristics and MFA measurements. Model performance was evaluated using Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analysis. Risk stratification was performed using an optimal threshold derived from training data, with subsequent Kaplan-Meier survival curve comparison between high- and low-risk cohorts.
resultsThe DL-based tumor model achieved C-indices of 0.787 (95% CI: 0.742-0.832) for disease-free survival (DFS) and 0.810 (95% CI: 0.768-0.852) for overall survival (OS) in internal validation. Integration of MFA with DL-derived tumor features yielded a multimodal model demonstrating enhanced predictive performance, with C-indices of 0.823 (OS) and 0.803 (DFS). Kaplan-Meier analysis revealed significant survival divergence between risk-stratified groups (log-rank p < 0.05).
conclusionThe multimodal fusion of DL-extracted tumor radiomics and mediastinal adiposity metrics represents a significant advancement in postoperative survival prediction for NSCLC patients, demonstrating superior prognostic capability compared to unimodal approaches.
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